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Combating COVID-19 with tissue engineering: a review.

ABSTRACT: The ongoing COVID-19 pandemic triggered by SARS-CoV-2 emerged from Wuhan, China, firstly in December 2019, as well spread to almost all around the world rapidly. The main reason why this disease spreads so many people in a short time is that the virus could be transmitted from an infected person to another by infected droplets. The new emergence of diseases usually may affect multiple organs; moreover, this disease is such an example. Numerous reported studies focus on acute or chronic organ damage caused by the virus. At this point, tissue engineering (TE) strategies can be used to treat the damages with its interdisciplinary approaches. Tissue engineers could design drug delivery systems, scaffolds, and especially biomaterials for the damaged tissue and organs. In this review, brief information about SARS-CoV-2, COVID-19, and epidemiology of the disease will be given at first. After that, the symptoms, the tissue damages in specific organs, and cytokine effect caused by COVID-19 will be described in detail. Finally, it will be attempted to summarize and suggest the appropriate treatments with suitable biomaterials for the damages via TE approaches. The aim of this review is to serve as a summary of currently available tissue damage treatments after COVID-19.

SUBMITTER: Aydin A

PROVIDER: S-EPMC7677604 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Combating COVID-19 with tissue engineering: a review.

Aydin Ayca A Cebi Gizem G Demirtas Zeynep Ezgi ZE Erkus Huseyin H Kucukay Aleyna A Ok Merve M Sakalli Latife L Alpdagtas Saadet S Gunduz Oguzhan O Ustundag Cem Bulent CB

Emergent materials 20201120 1

The ongoing COVID-19 pandemic triggered by SARS-CoV-2 emerged from Wuhan, China, firstly in December 2019, as well spread to almost all around the world rapidly. The main reason why this disease spreads so many people in a short time is that the virus could be transmitted from an infected person to another by infected droplets. The new emergence of diseases usually may affect multiple organs; moreover, this disease is such an example. Numerous reported studies focus on acute or chronic organ dam ...[more]

PMID: 33235976

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Project description:BackgroundMobile phone apps have been leveraged to combat the spread of COVID-19. However, little is known about these technologies' characteristics, technical features, and various applications in health care when responding to this public health crisis. The lack of understanding has led developers and governments to make poor choices about apps' designs, which resulted in creating less useful apps that are overall less appealing to consumers due to their technical flaws.ObjectiveThis review aims to identify, analyze, and categorize health apps related to COVID-19 that are currently available for consumers in app stores; in particular, it focuses on exploring their key technical features and classifying the purposes that these apps were designed to serve.MethodsA review of health apps was conducted using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The Apple Store and Google Play were searched between April 20 and September 11, 2020. An app was included if it was dedicated for this disease and was listed under the health and medical categories in these app stores. The descriptions of these apps were extracted from the apps' web pages and thematically analyzed via open coding to identify both their key technical features and overall purpose. The characteristics of the included apps were summarized and presented with descriptive statistics.ResultsOf the 298 health apps that were initially retrieved, 115 met the inclusion criteria. A total of 29 technical features were found in our sample of apps, which were then categorized into five key purposes of apps related to COVID-19. A total of 77 (67%) apps were developed by governments or national authorities and for the purpose of promoting users to track their personal health (9/29, 31%). Other purposes included raising awareness on how to combat COVID-19 (8/29, 27%), managing exposure to COVID-19 (6/29, 20%), monitoring health by health care professionals (5/29, 17%), and conducting research studies (1/29, 3.5%).ConclusionsThis study provides an overview and taxonomy of the health apps currently available in the market to combat COVID-19 based on their differences in basic technical features and purpose. As most of the apps were provided by governments or national authorities, it indicates the essential role these apps have as tools in public health crisis management. By involving most of the population in self-tracking their personal health and providing them with the technology to self-assess, the role of these apps is deemed to be a key driver for a participatory approach to curtail the spread of COVID-19. Further effort is required from researchers to evaluate these apps' effectiveness and from governmental organizations to increase public awareness of these digital solutions.

Project description:BackgroundResearch on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood.ObjectiveThis review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code.MethodsA search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as "generative adversarial networks" and "GANs," and application keywords, such as "COVID-19" and "coronavirus." The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included.ResultsThis review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51%) studies used chest X-ray images, while 21 (37%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4%) studies.ConclusionsStudies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs' performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications.

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Combating COVID-19 with tissue engineering: a review.

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Combating COVID-19 with tissue engineering: a review.

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